from torch import nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
from copy import deepcopy
from models.other_layers import *

__all__ = ['resnet50_cat_aux', 'resnet50_cat_aux2', 'resnet50_cat_aux3']

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model

class ResNet_aux(nn.Module):

    def __init__(self, base_model, num_classes, aux_classes):
        super(ResNet_aux,self).__init__()
        self.base = base_model
        self.base.fc = cat_linear(2048, num_classes)
        self.aux_fc = nn.Linear(2048, aux_classes)



    def forward(self, x):
        x = self.base.conv1(x)
        x = self.base.bn1(x)
        x = self.base.relu(x)
        x = self.base.maxpool(x)

        x = self.base.layer1(x)
        x = self.base.layer2(x)
        x = self.base.layer3(x)
        x = self.base.layer4(x)

        x = self.base.avgpool(x)

        x = x.view(x.size(0), -1)

        aux_x = self.aux_fc(x)
        x = self.base.fc(x)
        return x,aux_x

class ResNet_aux2(nn.Module):

    def __init__(self, base_model, num_classes, aux_classes):
        super(ResNet_aux2,self).__init__()
        self.base = base_model
        self.base.fc = cat_linear(2048, num_classes)
        self.aux_layer4 = deepcopy(self.base.layer4)
        self.aux_avgpool = deepcopy(self.base.avgpool)
        self.aux_fc = nn.Linear(2048, aux_classes)



    def forward(self, x):
        x = self.base.conv1(x)
        x = self.base.bn1(x)
        x = self.base.relu(x)
        x = self.base.maxpool(x)

        x = self.base.layer1(x)
        x = self.base.layer2(x)
        x = self.base.layer3(x)

        aux_x = self.aux_layer4(x)
        x = self.base.layer4(x)

        aux_x = self.aux_avgpool(aux_x)
        x = self.base.avgpool(x)

        aux_x = aux_x.view(aux_x.size(0), -1)
        x = x.view(x.size(0), -1)

        aux_x = self.aux_fc(aux_x)
        x = self.base.fc(x)
        return x,aux_x


class ResNet_aux3(nn.Module):

    def __init__(self, base_model, num_classes, aux_classes):
        super(ResNet_aux3,self).__init__()
        self.base = base_model
        self.base.fc = cat_linear(2048, num_classes)
        self.aux_fc1 = nn.Linear(2048, 1024)
        self.aux_fc2 = nn.Linear(1024, aux_classes)



    def forward(self, x):
        x = self.base.conv1(x)
        x = self.base.bn1(x)
        x = self.base.relu(x)
        x = self.base.maxpool(x)

        x = self.base.layer1(x)
        x = self.base.layer2(x)
        x = self.base.layer3(x)
        x = self.base.layer4(x)

        x = self.base.avgpool(x)

        x = x.view(x.size(0), -1)

        aux_x = self.aux_fc1(x)
        aux_x = self.aux_fc2(aux_x)
        x = self.base.fc(x)
        return x,aux_x


def resnet50_cat_aux( num_classes,aux_classes, pretrained=True):
    base_model = resnet50(pretrained=pretrained)
    model = ResNet_aux(base_model,num_classes=num_classes,aux_classes=aux_classes)
    return model

def resnet50_cat_aux2( num_classes,aux_classes, pretrained=True):
    base_model = resnet50(pretrained=pretrained)
    model = ResNet_aux2(base_model,num_classes=num_classes,aux_classes=aux_classes)
    return model

def resnet50_cat_aux3( num_classes,aux_classes, pretrained=True):
    base_model = resnet50(pretrained=pretrained)
    model = ResNet_aux3(base_model,num_classes=num_classes,aux_classes=aux_classes)
    return model

if __name__ == '__main__':

    # model = resnet50_cat(pretrained=True, num_classes=[5, 10])

    model = resnet50_cat_aux(pretrained=True,num_classes=[3,4],aux_classes=2)
    print model
    model.eval()
    # print model
    # split_layer4 = SplitLayer(model.layer4, 'layer4', 8)
    # print split_layer4
    #
    # print (split_layer4.layer4_copy0[0].conv1.weight != split_layer4.layer4_copy1[0].conv1.weight).sum()
    #
    # model.layer4 = split_layer4
    #
    # # # for i in base_model.fc.children():
    # # #     print i
    # # for item in model.state_dict():
    # #     print item, model.state_dict()[item].size()
    # #
    # # #
    x = torch.FloatTensor(3,3,224,224).zero_()
    x = torch.autograd.Variable(x)
    # # # import numpy as np
    # # #
    # # # attrs = torch.from_numpy(np.array([0,2,1]))
    # # #
    y = model(x)
    print y
    # #

